A photon counting CT imaging pipeline for cardiac phenotyping in APOE mice

Our new study develops an advanced imaging pipeline using photon-counting CT to assess cardiac structure & function in mouse models of apolipoprotein E gene variations. Results highlight genotype-specific cardiovascular differences and the impact of diet.

Allphin, A. J., Mahzarnia, A., Clark, D. P., Qi, Y., Han, Z. Y., Bhandari, P., Ghaghada, K. B., Badea, A., & Badea, C. T. (2023). Advanced photon counting CT imaging pipeline for cardiac phenotyping of apolipoprotein E mouse models. PLoS ONE 18(10): e0291733, https://doi.org/10.1371/journal.pone.0291733.


Leveraging 3D Bioprinting & Photon-Counting Computed Tomography for Regenerative Medicine

An exciting publication on our collaborative research focusing on the innovative applications of 3D Bioprinting and Photon-Counting CT.

Gil CJ, Evans CJ, Li L, Allphin AJ, Tomov ML, Jin L, Vargas M, Hwang B, Wang J, Putaturo V, Kabboul G, Alam AS, Nandwani RK, Wu Y, Sushmit A, Fulton T, Shen M, Kaiser JM, Ning L, Veneziano R, Willet N, Wang G, Drissi H, Weeks ER, Bauser-Heaton HD, Badea CT, Roeder RK, Serpooshan V. Leveraging 3d Bioprinting And Photon-Counting Computed Tomography to Enable Noninvasive Quantitative Tracking of Multifunctional Tissue Engineered Constructs. Adv Healthc Mater. 2023 Sep 14:e2302271. doi: 10.1002/adhm.202302271. PMID: 37709282.

Read the full paper here

A big thank you to our team and collaborators..

Deep Learning Denoising for Photon-Counting CT

Nadkarni, R.; Clark, D.P.; Allphin, A.J.; Badea, C.T. A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images. Tomography 20239, 1286-1302. https://doi.org/10.3390/tomography9040102

MCR toolkit

Our QIAL developed Multi-Channel CT Reconstruction (MCR) Toolkit addresses unique challenges associated with in vivo preclinical micro-CT and supporting the translation of these advancements to the clinical domain.

We have now published a paper in Medical Physics detailing our toolkit’s capabilities and functions:

Clark DP, Badea CT. MCR toolkit: A GPU-based toolkit for multi-channel reconstruction of preclinical and clinical x-ray CT data. Med. Phys.. 2023;1-22. https://doi.org/10.1002/mp.16532 

Co-Clinical Imaging

Our group contributed to these new papers from the co-clinical imaging research program (CIRP) :

1.Moore, S.M.; Quirk, J.D.; Lassiter, A.W.; Laforest, R.; Ayers, G.D.; Badea, C.T.; Fedorov, A.Y.; Kinahan, P.E.; Holbrook, M.; Larson, P.E.Z.; Sriram, R.; Chenevert, T.L.; Malyarenko, D.; Kurhanewicz, J.; Houghton, A.M.; Ross, B.D.; Pickup, S.; Gee, J.C.; Zhou, R.; Gammon, S.T.; Manning, H.C.; Roudi, R.; Daldrup-Link, H.E.; Lewis, M.T.; Rubin, D.L.; Yankeelov, T.E.; Shoghi, K.I. Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical ImagingTomography 20239, 995-1009. https://doi.org/10.3390/tomography9030081

2.Gammon, S.T.; Cohen, A.S.; Lehnert, A.L.; Sullivan, D.C.; Malyarenko, D.; Manning, H.C.; Hormuth, D.A.; Daldrup-Link, H.E.; An, H.; Quirk, J.D.; Shoghi, K.; Pagel, M.D.; Kinahan, P.E.; Miyaoka, R.S.; Houghton, A.M.; Lewis, M.T.; Larson, P.; Sriram, R.; Blocker, S.J.; Pickup, S.; Badea, A.; Badea, C.T.; Yankeelov, T.E.; Chenevert, T.L. An Online Repository for Pre-Clinical Imaging Protocols (PIPs)Tomography 20239, 750-758. https://doi.org/10.3390/tomography9020060


QIAL papers at SPIE Medical Imaging 2023

1)  A Allphin, R Nadkarni, D Clark, C T Badea. Ex vivo high-resolution hybrid micro-CT imaging using photon counting and energy integrating detectors. Proc. of SPIE Vol 12468, 124680V-1, 2023

2) DP Clark, FR Schwartz, A Euler, V Mergen, H Alkadhi, D Marin, CT Badea  Unsupervised learning of robust models for cardiac and photon-counting x-ray CT denoising, SPIE Medical Imaging 2023: Physics of Medical Imaging 12463, 329-337

3) AJ Allphin, DP Clark, T Thuering, P Bhandari, KB Ghaghada, CT Badea Spectral micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors, SPIE Medical Imaging 2023: Physics of Medical Imaging 12463, 153-159

Denoising dual-energy abdominal CT of obese patients

The purpose of this study was to evaluate a novel algorithm for noise reduction in obese patients for dual-source dual-energy (DE) CT abdominal imaging. We demonstrated that multi-channel denoising methods (RSKR, multi-energy (ME)-NLM) could reduce image noise and improve both objective image quality metrics like contrast-to-noise ratio (CNR) and subjective metrics like reader satisfaction.Fides R. Schwartz, Darin P. Clark, Francesca Rigiroli, Kevin Kalisz, Benjamin Wildman-Tobriner, Sarah Thomas, Joshua Wilson, Cristian T. Badea & Daniele Marin . Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patientsEur Radiol (2023). https://doi.org/10.1007/s00330-023-09644-7 

Spectral CT imaging using GaAs and CdTe photon counting detectors

In this study, we compared the performance of PC micro-CT imaging when using two types of PCDs. We have also demonstrated the ability of PCDs to separate a wide range of K-edge materials.

Alex Jeffrey Allphin, Darin P Clark, Thomas Thüring, Prajwal Bhandari, Ketan B Ghaghada, Cristian T Badea. Micro-CT imaging of multiple K-edge elements using GaAs and CdTe photon counting detectors. Physics in Medicine & Biology, 2023, DOI 10.1088/1361-6560/acc77e

Group Photo

QIAL members and the Bass Connections team, March 2023

Artist and T-shirts Designer: Nariman (Ali) Mahzarnia


Animal Models and Their Role in Imaging-Assisted Co-Clinical Trials

This overview paper describes the ten co-clinical trials of investigators from eleven institutions who are supported by the CIRP initiative and are members of the Animal Models and Co-clinical Trials (AMCT) Working Group.

Examples of MRI and micro-CT images in the p53/MCA model and the schematics of the preclinical arm of the clinical trial organized at Duke.

Peehl, D.M.; Badea, C.T.; Chenevert, T.L.; Daldrup-Link, H.E.; Ding, L.; Dobrolecki, L.E.; Houghton, A.M.; Kinahan, P.E.; Kurhanewicz, J.; Lewis, M.T.; Li, S.; Luker, G.D.; Ma, C.X.; Manning, H.C.; Mowery, Y.M.; O’Dwyer, P.J.; Pautler, R.G.; Rosen, M.A.; Roudi, R.; Ross, B.D.; Shoghi, K.I.; Sriram, R.; Talpaz, M.; Wahl, R.L.; Zhou, R. Animal Models and Their Role in Imaging-Assisted Co-Clinical TrialsTomography 20239, 657-680. https://doi.org/10.3390/tomography9020053